{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Comparison for Cognitive Models\n",
    "Part 1: Non-Hierarchical Model Comparison.\n",
    "\n",
    "by Lasse Elsemüller"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Introduction\" data-toc-modified-id=\"Introduction-1\">Introduction</a></span></li><li><span><a href=\"#Generative-Model-Definition\" data-toc-modified-id=\"Generative-Model-Definition-2\">Generative Model Definition</a></span><ul class=\"toc-item\"><li><span><a href=\"#Priors\" data-toc-modified-id=\"Priors-2.1\">Priors</a></span></li><li><span><a href=\"#Creating-Simulators\" data-toc-modified-id=\"Creating-Simulators-2.2\">Creating Simulators</a></span></li><li><span><a href=\"#Prior-Predictive-Checks\" data-toc-modified-id=\"Prior-Predictive-Checks-2.3\">Prior Predictive Checks</a></span></li></ul></li><li><span><a href=\"#Defining-the-Neural-Approximator\" data-toc-modified-id=\"Defining-the-Neural-Approximator-3\">Defining the Neural Approximator</a></span><ul class=\"toc-item\"><li><span><a href=\"#Defining-the-Configurator\" data-toc-modified-id=\"Defining-the-Configurator-3.1\">Defining the Configurator</a></span></li><li><span><a href=\"#Defining-the-Trainer\" data-toc-modified-id=\"Defining-the-Trainer-3.2\">Defining the Trainer</a></span></li></ul></li><li><span><a href=\"#Training-Phase\" data-toc-modified-id=\"Training-Phase-4\">Training Phase</a></span></li><li><span><a href=\"#Network-Validation\" data-toc-modified-id=\"Network-Validation-5\">Network Validation</a></span></li><li><span><a href=\"#Network-Application\" data-toc-modified-id=\"Network-Application-6\">Network Application</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from functools import partial\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "from scipy import stats\n",
    "\n",
    "import bayesflow as bf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "This tutorial series contains workflows for comparing competing probabilistic models via posterior model probabilities (PMPs) or Bayes Factors (BFs) with BayesFlow. We start with non-hierarchical model comparison in this tutorial (part 1), while [part 2](./Hierarchical_Model_Comparison_MPT.ipynb) will look at the modifications that allow us to compare hierarchical models. To keep the content concise, the focus will be on the model comparison steps themselves. For a comprehensive overview of the different functionalities BayesFlow has to offer, see the [\"Principled Amortized Bayesian Workflow for Cognitive Modeling\"](./LCA_Model_Posterior_Estimation.ipynb) tutorial notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generative Model Definition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial, we will compare simple Multinomial Processing Tree (MPT) models. They are a popular class of stochastic models in cognitive psychology aiming to explain observed categorical decision data by a branched structure of discrete latent processes. We embed the tutorial within the scenario of an old-new-recognition task. In this task, participants memorize a list of stimulus items (e.g., words) and indicate in a subsequent phase whether a presented stimulus was shown before ('old' decision) or is a distractor item ('new' decision)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More specifically, we compare two classic MPT models: The basic one-high-threshold (1HT) model and the popular two-high-threshold (2HT) model.\n",
    "\n",
    "The 1HT model can be considered as the simplest model formulation: For old items, it assumes that participants either recognize an item or if they do not, guess whether it is old and new. For new items, it assumes that participant directly initiate a guessing process.\n",
    "\n",
    "The 2HT model extends the process assumed for new items by proposing a similar process as for new items, such that participants either recognize a stimulus as new directly and only if they do not enter the guessing process. This model frequently explains categorical decision data much better than the 1HT model.\n",
    "\n",
    "For further information on MPT models and the 1HT and 2HT instantiations see [Erdfelder et al. (2009)](https://psycnet.apa.org/record/2009-21670-002)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By traversing the branches of the trees, we obtain the equations for each outcome category. For these equations, we encode 'old' items as 1 and 'new' items as 0. Further, the first index of the response probabilities indicates the stimulus type and the second the response. Thus, $p_{11}$ stands for the probability to correctly recognize a previously presented stimulus, while $p_{01}$ stands for a false alarm (identifying a distractor item as 'old').\n",
    "\n",
    "In order to make the 2HT model identifiable, we follow the convention of assuming equal probabilities for recognizing old items and identifying new items."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=img/1HT2HT.png width=\"1000\" height=\"500\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One-high-threshold (1HT) MPT model:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "p_{11} &= d + (1-d)*g \\\\\n",
    "p_{10} &= (1-d)*(1-g) \\\\\n",
    "p_{01} &= g \\\\\n",
    "p_{00} &= (1-g) \\\\\n",
    "x_1 &\\sim \\textrm{Bernoulli}(p_{11}) \\\\\n",
    "x_0 &\\sim \\textrm{Bernoulli}(p_{01})\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "\n",
    "Two-high-threshold (2HT) MPT model:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "p_{11} &= d + (1-d)*g \\\\\n",
    "p_{10} &= (1-d)*(1-g) \\\\\n",
    "p_{01} &= (1-d)*g \\\\\n",
    "p_{00} &= d + (1-d)*(1-g) \\\\\n",
    "x_1 &\\sim \\textrm{Bernoulli}(p_{11}) \\\\\n",
    "x_0 &\\sim \\textrm{Bernoulli}(p_{01})\n",
    "\\end{align}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Priors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our models only have two parameters: $d$ for recognition and $g$ for guessing. As both parameters represent probabilities, we choose moderately informative beta priors with 2 for both shape parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "PARAM_NAMES = [r\"$d$\", r\"$g$\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prior_fun(rng=None):\n",
    "    \"Samples a random parameter configuration from the prior distribution.\"\n",
    "    if rng is None:\n",
    "        rng = np.random.default_rng()\n",
    "\n",
    "    d = rng.beta(a=2, b=2)\n",
    "    g = rng.beta(a=2, b=2)\n",
    "    return np.r_[d, g]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The BayesFlow ``Prior`` wrapper provides us further utilities for inspecting our chosen parameter prior:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "prior = bf.simulation.Prior(prior_fun=prior_fun, param_names=PARAM_NAMES)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can sample from the constructed prior, with the argument ``batch_size`` governing the number of draws. For instance, calling the prior with ``batch_size=5`` will return a dictionary, containing, among others, an entry ``prior_draws`` which holds 5 random draws from the prior in the form of a $5 \\times 2$ matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'prior_draws': array([[0.60567859, 0.21211342],\n",
       "        [0.53302919, 0.50565267],\n",
       "        [0.30158864, 0.87688014],\n",
       "        [0.5178926 , 0.57265337],\n",
       "        [0.70948297, 0.88509065]]),\n",
       " 'batchable_context': None,\n",
       " 'non_batchable_context': None}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prior(batch_size=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note, that the prior also returned some other stuff, which allows for more flexible priors (e.g., parametric priors or prior sensitivity analysis). To inspect whether our chosen prior is sensible, we can conduct some prior predictive checks in the parameter space. The simplest one is to simply visualize our prior draws:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/radevs/anaconda3/envs/BayesFlowDev/lib/python3.10/site-packages/seaborn/axisgrid.py:64: UserWarning: The figure layout has changed to tight\n",
      "  self.fig.tight_layout(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = prior.plot_prior2d(n_samples=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that our beta priors symmetrically concentrate the probability mass around .5, but still consider more extreme parameter values possible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Simulators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we translate the model equations above into a simulator from which we can generate simulated observational data. Since both models are nested, we can use a single simulator function. For non-nested models, we would construct one function for each computational model.\n",
    "\n",
    "We will apply BayesFlow to the trial-level data, as this is much more instructive and generalizes to other applications, noting that traditional MPT models use aggregated data. We therefore do not directly implement the multinomial likelihood stated above (which would results in a single row per participant) but decompose it into Bernoulli draws to generate as many rows per participant as trials. As our binary category probabilities add up to 1, we only need the probabilities for old responses, $p_{11}$ and $p_{01}$.\n",
    "\n",
    "One could additionally add context variables here to include varying trial numbers for instance (see the [\"Principled Amortized Bayesian Workflow for Cognitive Modeling\"](https://github.com/stefanradev93/BayesFlow/blob/master/docs/source/tutorial_notebooks/LCA_Model_Posterior_Estimation.ipynb) tutorial)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "N_OBS = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mpt_simulator(theta, model, num_obs, rng=None, *args):\n",
    "    \"\"\"Simulates data from a 1HT or 2HT MPT model, assuming equal proportions of old and new stimuli.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    theta : np.ndarray of shape (num_parameters,)\n",
    "        Contains draws from the prior distribution for each parameter.\n",
    "    model : str, either \"1HT\" or \"2HT\"\n",
    "        Decides the model to generate data from.\n",
    "    num_obs : int\n",
    "        The number of observations (trials).\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    data     : np.ndarray of shape (num_obs, 2)\n",
    "        The generated data set. Contains two columns:\n",
    "            1. Stimulus type (0=\"new\", 1=\"old\")\n",
    "            2. Response (0=\"new\", 1=\"old\")\n",
    "    \"\"\"\n",
    "    if rng is None:\n",
    "        rng = np.random.default_rng()\n",
    "\n",
    "    obs_per_condition = int(np.ceil(num_obs / 2))\n",
    "\n",
    "    # Compute category probabilities per model\n",
    "    d, g = theta\n",
    "\n",
    "    if model == \"1HT\":\n",
    "        p_11 = d + (1 - d) * g\n",
    "        p_01 = g\n",
    "\n",
    "    elif model == \"2HT\":\n",
    "        p_11 = d + (1 - d) * g\n",
    "        p_01 = (1 - d) * g\n",
    "\n",
    "    else:\n",
    "        raise NotImplementedError(\"Model not understood\")\n",
    "\n",
    "    # Create a vector of stimulus types\n",
    "    stims = np.repeat([[1, 0]], repeats=obs_per_condition, axis=1).T\n",
    "\n",
    "    # Simulate responses\n",
    "    resp_old_items = rng.binomial(n=1, p=p_11, size=obs_per_condition)[:, np.newaxis]\n",
    "    resp_new_items = rng.binomial(n=1, p=p_01, size=obs_per_condition)[:, np.newaxis]\n",
    "    resp = np.concatenate((resp_old_items, resp_new_items), axis=0)\n",
    "\n",
    "    # Create final data set\n",
    "    data = np.concatenate((stims, resp), axis=1)\n",
    "\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now pass our custom prior and simulator functions to the ``GenerativeModel`` wrapper. Here, we use the ``partial`` function to provide the arguments for each model. If you provided context variables before, you could use a wrapper for your simulator function beforehand. In this case, specifying ``simulator_is_batched`` would not be necessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Performing 2 pilot runs with the 1HT model...\n",
      "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 2)\n",
      "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 100, 2)\n",
      "INFO:root:No optional prior non-batchable context provided.\n",
      "INFO:root:No optional prior batchable context provided.\n",
      "INFO:root:No optional simulation non-batchable context provided.\n",
      "INFO:root:No optional simulation batchable context provided.\n",
      "INFO:root:Performing 2 pilot runs with the 2HT model...\n",
      "INFO:root:Shape of parameter batch after 2 pilot simulations: (batch_size = 2, 2)\n",
      "INFO:root:Shape of simulation batch after 2 pilot simulations: (batch_size = 2, 100, 2)\n",
      "INFO:root:No optional prior non-batchable context provided.\n",
      "INFO:root:No optional prior batchable context provided.\n",
      "INFO:root:No optional simulation non-batchable context provided.\n",
      "INFO:root:No optional simulation batchable context provided.\n"
     ]
    }
   ],
   "source": [
    "model_1ht = bf.simulation.GenerativeModel(\n",
    "    prior=prior_fun,\n",
    "    simulator=partial(mpt_simulator, model=\"1HT\", num_obs=N_OBS),\n",
    "    name=\"1HT\",\n",
    "    simulator_is_batched=False,\n",
    ")\n",
    "\n",
    "model_2ht = bf.simulation.GenerativeModel(\n",
    "    prior=prior_fun,\n",
    "    simulator=partial(mpt_simulator, model=\"2HT\", num_obs=N_OBS),\n",
    "    name=\"2HT\",\n",
    "    simulator_is_batched=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now inspect all the components contained in our finished generative models by calling them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['prior_non_batchable_context', 'prior_batchable_context', 'prior_draws', 'sim_non_batchable_context', 'sim_batchable_context', 'sim_data'])\n"
     ]
    }
   ],
   "source": [
    "model_output = model_1ht(batch_size=5)\n",
    "print(model_output.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data batch: (5, 100, 2)\n",
      "First 3 rows in first data set:\n",
      "[[1 1]\n",
      " [1 1]\n",
      " [1 1]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of data batch:\", model_output[\"sim_data\"].shape)\n",
    "print(\"First 3 rows in first data set:\")\n",
    "print(model_output[\"sim_data\"][0, :3, :])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a last step that is specific to model comparison, we combine all generative models using the ``MultiGenerativeModel`` wrapper. This is necessary because during the training process, we want to generate data from not just one, but all candidate models. The wrapper assumes the common case of equal prior model probabilities, but we could also supply other probabilities via the ``model_probs`` argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "generative_models = bf.simulation.MultiGenerativeModel([model_1ht, model_2ht])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prior Predictive Checks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we fully implemented the generative models as simulators, we can conduct the final model building step by checking the faithfulness of the resulting data patterns. For this, we implement prior predictive checks on the data level in three steps: \n",
    "\n",
    "1. Simulate a large number of data sets (= participants) from each model\n",
    "2. Compute meaningful summary statistics (here: hit rates and false-alarms rates) for each model \n",
    "3. Plot the resulting data summaries for each model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Data simulation\n",
    "sim_ppcheck_1ht = model_1ht(batch_size=1000)\n",
    "sim_ppcheck_2ht = model_2ht(batch_size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. Summary statistics\n",
    "def get_rates(sim_data):\n",
    "    \"\"\"Get the hit rate and false alarm rate for each data set (= participant) in a batch of data\n",
    "    sets simulating binary decision (recognition) tasks.\n",
    "    Assumes first half of data to cover old items and second half to cover new items.\"\"\"\n",
    "\n",
    "    obs_per_condition = int(np.ceil(sim_data.shape[-2] / 2))\n",
    "    hit_rates = np.mean(sim_data[:, :obs_per_condition, 1], axis=-1)\n",
    "    fa_rates = np.mean(sim_data[:, obs_per_condition:, 1], axis=-1)\n",
    "\n",
    "    return hit_rates, fa_rates\n",
    "\n",
    "\n",
    "rates_1htm = get_rates(sim_ppcheck_1ht[\"sim_data\"])\n",
    "rates_2htm = get_rates(sim_ppcheck_2ht[\"sim_data\"])\n",
    "rates = [rates_1htm, rates_2htm]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3. Plot rates across all data sets\n",
    "fig = plt.figure(constrained_layout=True, figsize=(8, 6))\n",
    "subfigs = fig.subfigures(nrows=2, ncols=1)\n",
    "model_names = [\"1HT MPT Model\", \"2HT MPT Model\"]\n",
    "num_bins = 20\n",
    "bins = np.linspace(0.0, 1.0, num_bins + 1)\n",
    "\n",
    "for row, subfig in enumerate(subfigs):\n",
    "    subfig.suptitle(model_names[row], fontsize=18)\n",
    "    axs = subfig.subplots(nrows=1, ncols=2)\n",
    "    sns.histplot(rates[row][0].flatten(), bins=bins, kde=True, color=\"#8f2727\", alpha=0.9, ax=axs[0]).set(\n",
    "        title=\"Hit Rates\"\n",
    "    )\n",
    "    sns.histplot(rates[row][1].flatten(), bins=bins, kde=True, color=\"#8f2727\", alpha=0.9, ax=axs[1]).set(\n",
    "        title=\"False Alarm Rates\"\n",
    "    )\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unsurprisingly, we observe similar hit rates for both models, as they assume the same latent processes for old items. Their difference in assumptions concerning new items manifests in the false alarm rates: The symmetric beta prior on the $g$ parameter directly translates into false alarm rates around ~.5 for the 1HT model. For the 2HT model, the additional recognition stage set before the guessing process lowers the false alarm rate to ~.25."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining the Neural Approximator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We assured the faithfulness of our simulator and can move on to building a neural approximator for the Bayesian model comparison task. Our first network is a summary network that reduces the dimensionality of our data.[^1] We assume our data to be independent and identically distributed (iid) and thus choose a ``DeepSet`` network that is aligned to this probabilistic symmetry.\n",
    "\n",
    "[^1]: This is admittedly a slight overkill for our very simple models, since we could compute perfect summary statistics directly here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "summary_net = bf.summary_networks.DeepSet()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we choose the inference network for our current inference task. For model comparison, we select the ``PMPNetwork`` which approximates posterior model probabilities (that we could subsequently transform into Bayes factors if desired)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "inference_net = bf.inference_networks.PMPNetwork(num_models=2, dropout=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we use the ``AmortizedModelComparison`` wrapper to connect the two networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "amortizer = bf.amortizers.AmortizedModelComparison(inference_net, summary_net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the Configurator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use a configurator to mediate between the simulators and the amortizer containing the networks. It transforms data into a suitable format for the neural networks, which are here two elements: The simulated data sets and the indices of the generating model for each data set. For this, we will simply use the ``DefaultModelComparisonConfigurator`` which is automatically initialized by the trainer instance (see below). We will also use the configurator later on, when validating the trained network, for convenient data transformations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the Trainer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can reward ourselves for our hard work and bring all previous elements of our workflow together. We pass them to the ``Trainer`` class, which handles all aspects of the training process for us. If desired, we could also pass it a ``checkpoint_path`` where it regularly saves the trained network so we can reuse it. The consistency check assures us that there should be no major bugs preventing in our training workflow from simulating the data to updating the network weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Performing a consistency check with provided components...\n",
      "2023-09-09 16:48:10.901555: I tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:606] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n",
      "INFO:root:Done.\n"
     ]
    }
   ],
   "source": [
    "trainer = bf.trainers.Trainer(\n",
    "    amortizer=amortizer,\n",
    "    generative_model=generative_models,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ``summary`` function gives us a quick overview of the network component sizes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"amortized_model_comparison\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " pmp_network (PMPNetwork)    multiple                  9154      \n",
      "                                                                 \n",
      " deep_set (DeepSet)          multiple                  67466     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 76620 (299.30 KB)\n",
      "Trainable params: 76620 (299.30 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "amortizer.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Phase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our simple simulators are extremly fast, so we can use online training (simulating the data on the fly during training). Here, we use $10$ epochs with $500$ iterations each and a batch size of $64$ simulations. This means that we use $10 \\times 500 \\times 64 = 320,000$ unique simulations in total for training our neural network. We can do this because the simulators are trivial to implement and thus very efficient to run. Training should take a couple of seconds to complete."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "losses = trainer.train_online(epochs=10, iterations_per_epoch=500, batch_size=64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Right after training finishes, we can inspect how the loss evolved over the training duration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diag_plot = bf.diagnostics.plot_losses(train_losses=losses, moving_average=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the network picked up the important parts of the task very fast and plateaued afterwards, which indicates that we have had more than enough training steps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ability of our amortized networks to quickly process thousands of simulated data sets opens up new possibilities for validating our method prior to applying it. Let's first simulate some data from our models and use the configurator to quickly transform it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate 1000 validation data sets\n",
    "sim_data = generative_models(1000)\n",
    "\n",
    "# Use the configurator to transform the data structure\n",
    "sim_data_transformed = trainer.configurator(sim_data)\n",
    "\n",
    "# Get true indices and predicted PMPs from the trained network\n",
    "sim_indices = sim_data_transformed[\"model_indices\"]\n",
    "sim_preds = amortizer(sim_data_transformed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first ask the most important question: Do our approximated PMPs correspond to some ground-truth? We can approach this question by looking at the _calibration_. It measures the closeness of the PMPs to the true underlying probabilities of our simulated data.\n",
    "\n",
    "We assess it with ``plot_calibration_curves``, which provides us with three important pieces of information for each model:\n",
    "1. The calibration curve, where we bin the predicted PMPs and contrast the bin means with the true probability for the respective model in each bin\n",
    "2. The marginal histogram of the bins, which tells us how stable the calibration curve is by showing the fraction of predictions in each bin\n",
    "3. The expected calibration error (ECE), a numerical measure of the calibration curve's divergence that takes the binning distribution into account"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cal_curves = bf.diagnostics.plot_calibration_curves(true_models=sim_indices, pred_models=sim_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We observe a close alignment of the calibration curve to the diagonal without systematic over- or underconfidence. The ECE being close 0 also confirms that our neural approximator produces highly calibrated PMPs.\n",
    "We can further inspect our approximator by examing the confusion matrix for our simulated data sets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_matrix = bf.diagnostics.plot_confusion_matrix(true_models=sim_indices, pred_models=sim_preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that in ~72% of simulated data sets the underlying model is correctly detected. By increasing the training duration and/or size of the neural networks, we could check whether our classifier is performing suboptimally or we already reached the upper bound performance that our sparse data allow for. The excellent calibration that we observed before suggests the second option here. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network Application"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can apply our trained network to our observed data. To demonstrate this, we simulate some data from the 2HT model. We quickly redefine our generating process with a fixed random seed to obtain reproducible outcomes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 100, 2)\n"
     ]
    }
   ],
   "source": [
    "fixed_rng = np.random.default_rng(2023)\n",
    "prior_fixed = bf.simulation.Prior(prior_fun=partial(prior_fun, rng=fixed_rng), param_names=PARAM_NAMES)\n",
    "fake_data_generator = bf.simulation.GenerativeModel(\n",
    "    prior=prior_fixed,\n",
    "    simulator=partial(mpt_simulator, model=\"2HT\", num_obs=N_OBS, rng=fixed_rng),\n",
    "    skip_test=True,\n",
    "    simulator_is_batched=False,\n",
    ")\n",
    "\n",
    "fake_data = fake_data_generator(batch_size=1)[\"sim_data\"]\n",
    "print(fake_data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our simulated data already has the required (number of data sets, number of observations, number of variables) shape, so we can directly proceed and have a look at the hit rate and the false alarm rate of our fake participant:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.88]), array([0.02]))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_rates(fake_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we see very low false alarm rate that the 1HT model struggles to explain, so we would expect our neural approximator to assign higher evidence to the 2HT model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.04058154, 0.9594184 ], dtype=float32)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Way 1: Amortizer with dictionary input\n",
    "amortizer.posterior_probs({\"summary_conditions\": fake_data})[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.04058154 0.9594184 ]\n"
     ]
    }
   ],
   "source": [
    "# Way 2: Raw data into summary network -> inference network\n",
    "embeddings = summary_net(fake_data)\n",
    "preds = inference_net.posterior_probs(embeddings)[0]\n",
    "print(preds.numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the PMPs are in favor of the 2HT model. We assumed equal prior model probabilities, so the transformation of these results into a Bayes factor is straightforward:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23.641743\n"
     ]
    }
   ],
   "source": [
    "bayes_factor21 = preds[1] / preds[0]\n",
    "print(bayes_factor21.numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Corresponding to the PMPs, the Bayes factor assigns higher evidence to the 2HT model. Despite only having 100 binary observations at hand, the data are so untypical for the 1HT that the Bayes factor reflects the data being much more likely under the 2HT model compared to the 1HT model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations, you now know how to conduct amortized Bayesian model comparison with BayesFlow! When you feel ready to find out how to compare hierarchical models, continue with [Part 2](./Hierarchical_Model_Comparison_MPT.ipynb)."
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  }
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